Crime Pattern Analysis in Los Angeles (2020–2024)

INTRODUCTION

Crime poses significant challenges to society, impacting community reputation, individual well-being, and overall economic growth. It affects public safety, urban development, and investment potential, making crime analysis an essential component of urban management and law enforcement strategies.

According to the 2020 U.S. Census, Los Angeles (LA), California, is the second most populous city in the United States, with a population of 3,898,747 (Editor & Tikkanen, 2024). The city's socioeconomic diversity, rapid urbanization, and presence of informal settlements contribute to crime occurrence and escalation. LA has experienced various property crimes (burglary, theft, shoplifting) and violent crimes (assault, homicide, rape, lynching), which negatively impact public perception, tourism, trade, and economic stability.

This study aims to conduct a comprehensive spatiotemporal analysis of crime trends in Los Angeles from 2020 to 2024, leveraging data-driven methodologies to extract meaningful insights. The study follows a structured workflow, incorporating:

1. Data Preprocessing: Cleaning and structuring raw crime datasets for analysis.
2. Descriptive Statistical Analysis: Categorizing and summarizing crime data to identify trends and distributions.
3. Temporal Analysis: Conducting time-series analysis (2020-2024) to examine seasonal and annual crime patterns.
4. Predictive Modeling: Utilizing the Prophet forecasting model to predict future crime rates.
5. Spatial Analysis: Implementing heatmaps and geospatial clustering to identify crime hotspots.

1. Data Preprocessing

# Installing and Importing the required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objs as go

!pip install prophet
from prophet import Prophet

!pip install folium
from folium.plugins import HeatMap
from folium import features

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# Loading crime data
crime_data = pd.read_csv('Crime_Data_from_2020_to_Present.csv')
crime_data.describe()

	DR_NO	TIME OCC	AREA	Rpt Dist No	Part 1-2	Crm Cd	Vict Age	Premis Cd	Weapon Used Cd	Crm Cd 1	Crm Cd 2	Crm Cd 3	Crm Cd 4	LAT	LON
count	9.786280e+05	978628.000000	978628.000000	978628.000000	978628.000000	978628.000000	978628.000000	978613.000000	325959.000000	978617.000000	68816.000000	2309.000000	64.00000	978628.000000	978628.000000
mean	2.196564e+08	1338.802627	10.702561	1116.686084	1.404785	500.810635	29.122904	306.181502	363.815372	500.564847	958.156344	984.192724	991.21875	33.995399	-118.081108
std	1.290395e+07	651.622947	6.107280	610.836054	0.490851	206.309796	21.961531	218.908131	123.673988	206.107451	110.251477	51.506344	27.06985	1.640056	5.684520
min	8.170000e+02	1.000000	1.000000	101.000000	1.000000	110.000000	-4.000000	101.000000	101.000000	110.000000	210.000000	310.000000	821.00000	0.000000	-118.667600
25%	2.106073e+08	900.000000	5.000000	589.000000	1.000000	331.000000	0.000000	101.000000	311.000000	331.000000	998.000000	998.000000	998.00000	34.014600	-118.430500
50%	2.208116e+08	1420.000000	11.000000	1141.000000	1.000000	442.000000	30.000000	203.000000	400.000000	442.000000	998.000000	998.000000	998.00000	34.058900	-118.322500
75%	2.309110e+08	1900.000000	16.000000	1617.000000	2.000000	626.000000	44.000000	501.000000	400.000000	626.000000	998.000000	998.000000	998.00000	34.164900	-118.273900
max	2.499253e+08	2359.000000	21.000000	2199.000000	2.000000	956.000000	120.000000	976.000000	516.000000	956.000000	999.000000	999.000000	999.00000	34.334300	0.000000

# Dropping irrelevant multiple columns
columns_to_drop = ['DR_NO', 'Date Rptd', 'TIME OCC', 'AREA', 'Rpt Dist No', 'Part 1-2', 'Crm Cd','Mocodes', 'Premis Cd', 'Weapon Used Cd', 'Weapon Desc', 'Status', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street' ]
crime_data = crime_data.drop(columns=columns_to_drop)
crime_data

	DATE OCC	AREA NAME	Crm Cd Desc	Vict Age	Vict Sex	Vict Descent	Premis Desc	Status Desc	LOCATION	LAT	LON
0	03/01/2020 12:00:00 AM	Wilshire	VEHICLE - STOLEN	0	M	O	STREET	Adult Arrest	1900 S LONGWOOD AV	34.0375	-118.3506
1	02/08/2020 12:00:00 AM	Central	BURGLARY FROM VEHICLE	47	M	O	BUS STOP/LAYOVER (ALSO QUERY 124)	Invest Cont	1000 S FLOWER ST	34.0444	-118.2628
2	11/04/2020 12:00:00 AM	Southwest	BIKE - STOLEN	19	X	X	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	Invest Cont	1400 W 37TH ST	34.0210	-118.3002
3	03/10/2020 12:00:00 AM	Van Nuys	SHOPLIFTING-GRAND THEFT ($950.01 & OVER)	19	M	O	CLOTHING STORE	Invest Cont	14000 RIVERSIDE DR	34.1576	-118.4387
4	08/17/2020 12:00:00 AM	Hollywood	THEFT OF IDENTITY	28	M	H	SIDEWALK	Invest Cont	1900 TRANSIENT	34.0944	-118.3277
...	...	...	...	...	...	...	...	...	...	...	...
978623	07/23/2024 12:00:00 AM	Wilshire	VEHICLE - STOLEN	0	NaN	NaN	STREET	Invest Cont	4000 W 23RD ST	34.0362	-118.3284
978624	01/15/2024 12:00:00 AM	Central	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	0	X	X	HOTEL	Invest Cont	1300 W SUNSET BL	34.0685	-118.2460
978625	07/19/2024 12:00:00 AM	Devonshire	TRESPASSING	0	X	X	MTA - ORANGE LINE - CHATSWORTH	Invest Cont	10000 OLD DEPOT PLAZA RD	34.2500	-118.5990
978626	04/24/2024 12:00:00 AM	Southwest	ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	70	F	W	SIDEWALK	Invest Cont	FLOWER ST	34.0215	-118.2868
978627	08/12/2024 12:00:00 AM	Van Nuys	VEHICLE - STOLEN	0	NaN	NaN	PARKING LOT	Invest Cont	6900 VESPER AV	34.1961	-118.4510

978628 rows × 11 columns

#Dropping the NaN values
crime_data = crime_data.dropna()
#Printing all the counts of NaN values
na_counts = crime_data.isna().sum()
print(na_counts)

DATE OCC        0
AREA NAME       0
Crm Cd Desc     0
Vict Age        0
Vict Sex        0
Vict Descent    0
Premis Desc     0
Status Desc     0
LOCATION        0
LAT             0
LON             0
dtype: int64

2. Descriptive Statistical Analysis

crime_data = crime_data.copy()

# Defining categories based on keywords
def categorize_crime(description):
    if any(keyword in description for keyword in ['ASSAULT', 'BATTERY']):
        return 'Assault/Battery'
    elif any(keyword in description for keyword in ['BURGLARY', 'THEFT', 'SHOPLIFTING', 'STOLEN']):
        return 'Theft/Burglary'
    elif any(keyword in description for keyword in ['ARSON']):
        return 'Arson'
    elif any(keyword in description for keyword in ['CHILD', 'PORNOGRAPHY']):
        return 'Child-related Crimes'
    elif any(keyword in description for keyword in ['ROBBERY']):
        return 'Robbery'
    elif any(keyword in description for keyword in ['FRAUD', 'EMBEZZLEMENT']):
        return 'Fraud/Financial Crimes'
    elif any(keyword in description for keyword in ['FIREARMS', 'WEAPONS', 'SHOTS']):
        return 'Firearm/Weapon Offense'
    else:
        return 'Other'

# Applying the categorization
crime_data.loc[:, 'Crime Category 1'] = crime_data['Crm Cd Desc'].apply(categorize_crime)

# Displaying the crime category
print(crime_data['Crime Category 1'].unique())

['Theft/Burglary' 'Assault/Battery' 'Other' 'Child-related Crimes'
 'Robbery' 'Fraud/Financial Crimes' 'Arson' 'Firearm/Weapon Offense']

# Counting the records for each crime category
crime_category_counts = crime_data['Crime Category 1'].value_counts()

# Plotting a bar chart
plt.figure(figsize=(10, 6))
bar_chart = crime_category_counts.plot(kind='bar', color='skyblue', edgecolor='black')

# Adding titles and labels
plt.title('Count of Records by Crime Category', fontsize=16)
plt.xlabel('Crime Category', fontsize=14)
plt.ylabel('Record Count', fontsize=14)
plt.xticks(rotation=45, ha='right', fontsize=12) 

for index, value in enumerate(crime_category_counts):
    plt.text(index, value + 5, str(value), ha='center', fontsize=10, color='black')

plt.tight_layout()  
plt.show()

# Categorizing 'Vict Age' into 10-year intervals
age_bins = list(range(0, 101, 10))  
labels = [f'{i}-{i+9}' for i in age_bins[:-1]]  
crime_data['Age Category'] = pd.cut(crime_data['Vict Age'], bins=age_bins, labels=labels, right=False)

# Counting the number of crimes per age category
age_category_count = crime_data['Age Category'].value_counts().sort_index()

# Ploting the results
plt.figure(figsize=(10, 6))
age_category_count.plot(kind='bar', color='skyblue')
plt.title('Number of victims by Age Category')
plt.xlabel('Age Category')
plt.ylabel('Crime Count')
plt.xticks(rotation=45)

for index, value in enumerate(age_category_count):
    plt.text(index, value + 5, str(value), ha='center', fontsize=10, color='black')
plt.show()

# Counting the number of crimes for each victim sex
victim_sex_count = crime_data['Vict Sex'].value_counts()

# Plotting the results
plt.figure(figsize=(8, 5))
victim_sex_count.plot(kind='bar', color='skyblue')
plt.title('Number of Crimes Vs Victim Sex')
plt.xlabel('Victim Sex')
plt.ylabel('Crime Count')
plt.xticks(rotation=0)  
for index, value in enumerate(victim_sex_count):
    plt.text(index, value + 5, str(value), ha='center', fontsize=10, color='black')

plt.show()

# Grouping the data by day of the week and count the number of incidents on each day
crime_data['DATE OCC'] = pd.to_datetime(crime_data['DATE OCC'], format='%m/%d/%Y %I:%M:%S %p', errors='coerce')
crime_data['Day_of_Week'] = crime_data['DATE OCC'].dt.day_name()
# Count the records for each crime category
crime_category_counts = crime_data['Day_of_Week'].value_counts()

# Plotting a bar chart
plt.figure(figsize=(10, 6))
bar_chart = crime_category_counts.plot(kind='bar', color='skyblue', edgecolor='black')

# Adding titles and labels
plt.title('Crimes by Day of the Week', fontsize=16)
plt.xlabel('Day of Week', fontsize=14)
plt.ylabel('Number of Incidents', fontsize=14)
plt.xticks(rotation=45, ha='right', fontsize=12) 

for index, value in enumerate(crime_category_counts):
    plt.text(index, value + 5, str(value), ha='center', fontsize=10, color='black')

plt.tight_layout()  
plt.show()

3. Temporal Analysis

# Converting date column to datetime format

crime_data['Year'] = crime_data['DATE OCC'].dt.year
crime_data['Year-Month'] = crime_data['DATE OCC'].dt.to_period('M')

# Grouping by 'Year-Month' and count the incidents for each period
monthly_crime_counts = crime_data.groupby('Year-Month').size()

# Plotting the number of crime incidents by Year-Month
plt.figure(figsize=(12, 6))
monthly_crime_counts.plot(kind='line', marker='o', color='skyblue', linewidth=2)

# Adding labels and title
plt.xlabel('Year-Month')
plt.ylabel('Number of Incidents')
plt.title('Number of Crime Incidents by Year-Month')

# Customizing the plot
plt.grid(True)
plt.xticks(rotation=45) 
plt.tight_layout()  

# Displaying the plot
plt.show()

4.Predictive Modeling

#Grouping data by year-month to get monthly crime counts 
#and defining year_month as 'ds' and Crime_count as 'y'
monthly_crime_count = crime_data.groupby('Year-Month').size().reset_index(name='Count')

monthly_crime_count['Year-Month'] = pd.to_datetime(monthly_crime_count['Year-Month'].astype(str))
monthly_crime_count.columns = ['ds', 'y']
monthly_crime_count.head()

	ds	y
0	2020-01-01	16725
1	2020-02-01	15586
2	2020-03-01	14309
3	2020-04-01	13505
4	2020-05-01	14915

# Initializing and fit the Prophet model
model = Prophet()
model.fit(monthly_crime_count)

# Creating a DataFrame for future predictions (remaining months of 2024)
future = model.make_future_dataframe(periods=4, freq='M')
forecast = model.predict(future)

# Displaying the forecast results
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(13)

05:59:10 - cmdstanpy - INFO - Chain [1] start processing
05:59:11 - cmdstanpy - INFO - Chain [1] done processing

	ds	yhat	yhat_lower	yhat_upper
48	2024-01-01	14885.576198	10795.613319	18773.847838
49	2024-02-01	13425.375931	9721.432351	17479.876831
50	2024-03-01	13911.225252	9979.239810	17942.995117
51	2024-04-01	12776.488784	8773.805889	16715.204070
52	2024-05-01	12728.544858	9035.595705	16688.040021
53	2024-06-01	12612.610423	8822.706444	16479.590835
54	2024-07-01	13020.227321	9299.558642	16832.479950
55	2024-08-01	12634.156872	8841.442804	16464.512741
56	2024-09-01	10627.285262	6695.002871	14799.672453
57	2024-09-30	12226.569253	8299.856286	15939.147945
58	2024-10-31	13068.577006	9148.468207	16879.194605
59	2024-11-30	15504.770064	11553.225510	19377.436226
60	2024-12-31	14322.701267	10018.081385	18105.088780

# Plotting traces for actual and forecasted data
trace_actual = go.Scatter(
    x=monthly_crime_count['ds'],
    y=monthly_crime_count['y'],
    mode='lines+markers',
    name='Actual Crime Count',
    marker=dict(color='blue'),
    line=dict(color='blue', width=2)
)

trace_forecast = go.Scatter(
    x=forecast['ds'],
    y=forecast['yhat'],
    mode='lines',
    name='Forecasted Crime Count',
    marker=dict(color='red'),
    line=dict(color='red', width=2)
)

trace_upper = go.Scatter(
    x=forecast['ds'],
    y=forecast['yhat_upper'],
    mode='lines',
    name='Forecast Upper Bound',
    line=dict(color='grey', dash='dash'),
    showlegend=False
)

trace_lower = go.Scatter(
    x=forecast['ds'],
    y=forecast['yhat_lower'],
    mode='lines',
    name='Forecast Lower Bound',
    line=dict(color='grey', dash='dash'),
    fill='tonexty',  # Fill between the upper and lower bounds
    fillcolor='rgba(128, 128, 128, 0.2)',
    showlegend=False
)

# Combining the traces into a figure
data = [trace_actual, trace_forecast, trace_upper, trace_lower]

# Layout customization
layout = go.Layout(
    title='Actual vs Forecasted Monthly Crime Count',
    xaxis=dict(title='Date'),
    yaxis=dict(title='Crime Count'),
    hovermode='closest'
)

# Creating the figure and plot
fig = go.Figure(data=data, layout=layout)
fig.show()

5. Spatial Analysis

# Checking if 'LAT' and 'LON' columns are numeric and adding them to a list
crime_data['LAT'] = pd.to_numeric(crime_data['LAT'], errors='coerce')
crime_data['LON'] = pd.to_numeric(crime_data['LON'], errors='coerce')
crime_locations = crime_data[['LAT', 'LON']].values.tolist()

# Defining map bounds for LA area
min_lat, max_lat = 33.5, 34.5  
min_lon, max_lon = -119.0, -117.5  
bounds = [[min_lat, min_lon], [max_lat, max_lon]]

# Initializing a Folium map centered on LA with fixed zoom limits
map = folium.Map(
    location=[34.05, -118.25], 
    zoom_start=11,
    min_zoom=10,
    max_zoom=14,
    max_bounds=True  
)

# Adding the heat map layer
HeatMap(crime_locations, radius=10, blur=10, max_zoom=13, opacity=0.2).add_to(map)

# Adding a custom legend
legend_html = """
<div style="
    position: fixed;
    bottom: 50px;
    left: 50px;
    width: 200px;
    height: 120px;
    background-color: white;
    border:2px solid grey;
    z-index:9999;
    font-size:14px;
    padding: 10px;
    ">
    <b>Crime Density Legend</b><br>
    <i style="background: blue; width: 10px; height: 10px; display: inline-block;"></i> Low Density<br>
    <i style="background: green; width: 10px; height: 10px; display: inline-block;"></i> Medium Density<br>
    <i style="background: orange; width: 10px; height: 10px; display: inline-block;"></i> High Density<br>
    <i style="background: red; width: 10px; height: 10px; display: inline-block;"></i> Very High Density<br>
</div>
"""
map.get_root().html.add_child(features.Element(legend_html))

# Adding a custom title
title_html = """
<div style="
    position: fixed;
    top: 10px;
    left: 50%;
    transform: translateX(-50%);
    z-index: 1000;
    background-color: white;
    padding: 10px;
    font-size: 20px;
    font-weight: bold;
    border: 2px solid grey;
    border-radius: 5px;
">
    Crime Heatmap of Los Angeles
</div>
"""
map.get_root().html.add_child(features.Element(title_html))

# Displaying the map
map

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Reference

Editor, W. D., & Tikkanen, A. (2024, September 3). List of the largest U.S. cities by population. Encyclopædia Britannica. https://www.britannica.com/topic/Whats-the-largest-US-city-by-population